Vision-based on-street parked vehicle detection via normalized-view classifiers and temporal filtering

ABSTRACT

A system for estimating parking occupancy includes a vehicle-detection device including an adjustable mast supporting an image capture device at a select height. The image capture device acquires video of a current parking area. A computer processor in communication with the image capture device is configured to receive the video data and define a region of interest in the video data. The processor is further configured to perform a spatial transform on the ROI to transform the ROI to a normalized geometric space. The processor is further configured to apply features of a detected object in the normalized geometric space to a vehicle classifier—previously trained with samples acquired from a normalized camera perspective similar to the normalized geometric space—and determine the occupancy of the current parking area using an output of the classifier.

BACKGROUND

The present disclosure teaches a camera-based system and method forestimating parking occupancy where the camera may be mounted on a mobileplatform selectively deployable for temporary operation in a parkingarea for data collection. The disclosure contemplates use betweenmultiple on-street parking environments, but is amendable to parkinglots and other like environments.

One challenge that parking management companies face while managingparking operations is an accurate occupancy determination and futureprediction capability about parking trends. Occupancy determination canbe used, for example, for parking guidance, while occupancy predictioncan be used to derive a dynamic pricing strategy for the managed parkingarea. This pricing strategy can require data on parking space usagepatterns, which can also depend on the time of day, season, and/orscheduled events. To collect this occupancy data in a meteredenvironment, some parking management companies use parking meter paymentdata as a surrogate of parking occupancy. The parking meter payment datacan be insufficient because vehicles do not always park according to theexact time the meter is paid for, and some vehicles skip payment whenthey park at a meter that is still active after a previous vehicledeparted.

The parking management company can alternatively monitor the parkingspaces in the parking area. Existing methods for monitoring parkingspaces and tracking vehicles occupying the spaces include sensor-basedsolutions. For example, “puck-style” sensors and ultrasonic ceiling orin-ground sensors output a binary signal when a vehicle is detected inthe parking area. A disadvantage associated with these sensor-basedmethods is a high cost for installation and maintenance of the sensors.Therefore, camera monitoring systems were recently developed to detectand track vehicles by processing image frames acquired from a fixedvideo camera. Similar to the sensor-based solution, this technology wasdesigned for permanent installation at the specific parking area beingmonitored. Therefore, an application of the occupancy data collectedtherefrom is limited to that specific parking area. Furthermore, acontinuous collection of this data may not be necessary if the parkingtrends do not change over time. In this scenario, an installation of themonitoring system may not provide a substantial return on theinvestment.

A mobile parking occupancy estimation system and method is desired whichis rapidly deployable for temporary operations between sites and isadapted to gather occupancy data from each specific site over a shortperiod of time (e.g., a few days or week(s)). However, one foreseenchallenge associated with a mobile system is that it would have tooperate without receiving site-specific training for the specificparking area and/or configuration. One aspect of the existing cameramonitoring system is that it typically acquires several days of videodata to train a classifier used with the system. Unlike the existingstationary system, the mobile system cannot train a vehicle classifierusing a constant background, as the background changes fromsite-to-site. The training can be considered necessary to maintainaccuracy. However, where a system is desired to only temporarily collectdata at the specific parking area, this site-specific training can betime consuming and exceed the duration that the mobile system is locatedat the site. In other words, the mobile system may not be provided thetime necessary to ramp up to a suitable accuracy level for the specificparking area.

Because this disclosure anticipates a portable device that is moveablefrom site-to-site, each for a short period of time and possibly withoutreturning, it becomes impractical for the site-specific training to workin this setting. Accordingly, a scalable system is desired whichrequires little to no site-specific training, re-training ofclassifiers, or parameter tuning, but one which still meets the desiredaccuracy levels. A selectively mobile system and method is desired whichis operative to transform the different parking areas to a generallycommon view domain and train a classifier in the common view domain forimproving accuracy.

INCORPORATION BY REFERENCE

The disclosures of co-pending and commonly assigned U.S. applicationSer. No. 13/922,091, entitled, “A Method For Available Parking DistanceEstimation Via Vehicle Side Detection”, by Orhan Bulan, et al., filedJun. 19, 2013; U.S. application Ser. No. 13/835,386, entitled“Two-Dimensional And Three-Dimensional Sliding Window-Based Methods AndSystems For Detecting Vehicles”, by Bulan et al., filed Mar. 15, 2013;U.S. patent application Ser. No. 13/913,606, entitled, “PrecipitationRemoval for Vision-Based Parking Management Systems”, by Wencheng Wu, etal., filed Jun. 10, 2013; and US Publication No. 2014/0046874, entitled“Real Time Dynamic Vehicle Price Management Methods, Systems AndProcessor-Readable Media”, by Faming Li, et al., filed Aug. 8, 2012 areeach totally incorporated herein by reference.

BRIEF DESCRIPTION

One embodiment of the disclosure relates to a method for estimatingparking occupancy in a current parking area. The method includesdeploying an image capture device in the current parking area. Themethod includes defining a current region of interest (ROI) within acamera field of view of the current parking area. The method includesacquiring a sequence of frames captured by the image capture device. Themethod includes performing a spatial transform on the current ROI totransform the current ROI in the camera field of view to a normalizedgeometric space. The method includes detecting at least one object inthe normalized geometric space. The method includes selecting a vehicleclassifier previously trained with samples acquired from a normalizedcamera perspective similar to the normalized geometric space. The methodincludes determining occupancy of the current parking area by applyingextracted features of the detected object to the classifier.

Another embodiment of the disclosure relates to a system for estimatingparking occupancy. The system includes a computer device including amemory in communication with a processor configured to deploy an imagecapture device in the current parking area and define a current regionof interest (ROI) within a camera field of view of the current parkingarea. The processor is further configured to acquire a sequence offrames captured by the image capture device. The processor performs aspatial transform on the current ROI to transform the current ROI in thecamera field of view to a normalized geometric space and detect at leastone object in the normalized geometric space. The processor furtherselects a vehicle classifier—previously trained with samples acquiredfrom a normalized camera perspective similar to the normalized geometricspace—and determines an occupancy of the current parking area byapplying extracted features of the detected object to the classifier.

Another embodiment of the disclosure relates to a system for estimatingparking occupancy. The system includes a vehicle-detection deviceincluding at least one image capture device for acquiring video of acurrent parking area and an adjustable mast supporting the at least oneimage capture device at a select height. The system further includes acomputer processor in communication with the image capture device. Thecomputer processor is configured to receive the video data and define aregion of interest in the video data. The processor is furtherconfigured to perform a spatial transform on the ROI to transform theROI to a normalized geometric space. The processor is further configuredto apply features of a detected object in the normalized geometric spaceto a vehicle classifier—previously trained with samples acquired from anormalized camera perspective similar to the normalized geometricspace—and determine the occupancy of the current parking area using anoutput of the classifier.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an overview of the present method.

FIG. 2 is a schematic illustration of a camera-based system forestimating parking occupancy using information acquired from avehicle-detection device.

FIG. 3 is an example vehicle-detection device selectively deployable ina number of different parking areas.

FIG. 4A-8 is a flowchart describing a method for estimating parkingoccupancy using the occupancy determination unit of FIG. 2.

FIG. 5A-F illustrates sample input and output of the view-normalizedtraining process.

FIG. 6A-C illustrates a projective correction mapping procedure.

FIGS. 7A-8 shows an acquired image frame before and after a temporalfiltering process is performed on it.

FIG. 7C shows a defined ROI segment virtually outlined in the givenfiltered image of FIG. 7B.

FIG. 7D shows a view-normalized ROI converting the ROI segment in FIG.7C.

DETAILED DESCRIPTION

The present disclosure teaches a camera-based system and method forestimating parking occupancy using a mobile vehicle-detection deviceselectively deployable in a number of different parking areas. In otherwords, the vehicle-detection device is adapted to movesite-to-site—i.e., rotate between different parking locations—to collectlocal parking occupancy information. Alternatively, the camera-basedsystem may be used for parking occupancy detection in settings where thecamera is mounted in a substantially stationary manner.

FIG. 1 is an overview of a method 10 for estimating parking occupancyusing the vehicle-detection device. The method starts at S12. At S14,the system acquires video data of at least one representative parkingarea and processes the data for training a set of global classifiers 15via machine learning and view-normalization. In other words, trainingsamples are collected from image frames captured at example parkingareas and are view-normalized to at least one pre-determined geometricspace (e.g., front-view or rear-view). The various classifiers for theat least-one pre-determined geometric space can each be trained usingvideo frames captured from various heights and angles relative to theparking areas while keeping the general pose relative to the parkedvehicles (e.g., front-view or rear-view) the same followed by aview-normalization to the at least-one pre-determined geometric space,and are thus trained using vehicles captured at a certain pre-determinedgeometric space.

When the vehicle-detection device, of the system, is positioned fordeployment at a specific parking area of interest, a region of interest(“current ROI”) is defined at S16 within the video camera field of view.Using the camera pose/orientation (i.e., front-view or rear-viewrelative to the parked vehicles at the parking area), one of thepreviously trained classifiers is selected from the set of availableglobal classifiers at S18. Mainly, the selected classifier is one thatwas trained using vehicle samples extracted from image frames that werecaptured from a similar pose/orientation.

At the deployment stage, video data is acquired from the parkingarea-of-interest at S20. A temporal filtering analysis can be performedon the image frames to eliminate transient outliers at S22. Mainly, thetemporal filtering can generate filtered image frames that essentiallyrepresent background and parked vehicle image segments. Next, a spatialtransform is performed on the current ROI to transform the current ROIin the camera field of view to a normalized geometric space at S24. Thespatial transform generates a projective correction on the filteredframes, and extracts segments of the frames corresponding to thenormalized view of the current ROI. At S26, a camera-based stationaryvehicle detection is performed on the transformed frames. In otherwords, the normalized geometric space is searched for at least onestationary object. Extracted features of the detected object are appliedto the classifier selected at S18. As mentioned supra, this classifierwas previously trained with samples acquired from a normalized geometricspace. The results of the classifier provide the occupancy informationof the current parking area. Optionally, a temporal correlation can beperformed on the image content of the normalized views and/or thetemporal occupancy information at S28. The results of the temporalcorrelation can be merged with the current occupancy information(generated at S26) to yield final occupancy information corresponding tothe current ROI at S30. The collected parking occupancy information (andother information such as payment information) can then be used toderive an appropriate on-street parking pricing strategy for a parkingmanagement company. The method ends at S32.

FIG. 2 is a schematic illustration of a camera-based system 100 forestimating parking occupancy. The system 100 includes an occupancydetermination unit 102 and a vehicle-detection device 104, which isselectively mobile, linked together by communication links, referred toherein as a network. In one embodiment, the system 100 may be in furthercommunication with a user device 106. These components are described ingreater detail below.

The occupancy determination unit 102 illustrated in FIG. 2 includes acontroller 110 that is part of or associated with the occupancydetermination unit 102. The exemplary controller 110 is adapted forcontrolling an analysis of video data received by the system 100. Thecontroller 110 includes a processor 112, which controls the overalloperation of the occupancy determination unit 102 by execution ofprocessing instructions that are stored in memory 114 connected to theprocessor 112.

The memory 114 may represent any type of tangible computer readablemedium such as random access memory (RAM), read only memory (ROM),magnetic disk or tape, optical disk, flash memory, or holographicmemory. In one embodiment, the memory 114 comprises a combination ofrandom access memory and read only memory. The digital processor 112 canbe variously embodied, such as by a single-core processor, a dual-coreprocessor (or more generally by a multiple-core processor), a digitalprocessor and cooperating math coprocessor, a digital controller, or thelike. The digital processor, in addition to controlling the operation ofthe occupancy determination unit 102, executes instructions stored inmemory 114 for performing the parts of the method outlined in FIGS. 1and 4A-B. In some embodiments, the processor 112 and memory 114 may becombined in a single chip.

The occupancy determination unit 102 may be embodied in a networkeddevice, such as an image capture device 105 supported by thevehicle-detection device 104, although it is also contemplated that theoccupancy determination unit 102 may be located elsewhere on a networkto which the system 100 is connected, such as on a central server, anetworked computer, or the like, or distributed throughout the networkor otherwise accessible thereto. In other words, the processing can beperformed within the image capture device 105 on site or in a centralprocessing offline or server computer after transferring the video datathrough a network. In one embodiment, the image capture device 105 canbe adapted to relay and/or transmit the video data to the occupancydetermination unit 102. In another embodiment, the video data 130 may beinput from any suitable source, such as a workstation, a database, amemory storage device, such as a disk, or the like.

The image capture device 105 is in communication with the controller 110containing the processor 112 and memories 114.

The stages disclosed herein are performed by the processor 112 accordingto the instructions contained in the memory 114. In particular, thememory 114 stores a set of previously trained classifiers 116, eachtrained to determine vehicles and/or occupancy of the current parkingarea using extracted features of a detected object; a ROI determinationmodule 118, which defines a ROI and determines a spatial transform thatcan be applied on the current ROI to transform the current ROI in thecamera field of view to a normalized geometric space; a classifierselection module 120, which selects a vehicle classifier from the setbeing previously trained from a normalized camera perspective similar tothe normalized geometric space; a video acquisition/buffering module122, which acquires a sequence of frames captured by the image capturedevice included on the vehicle-detection device; a vehicle detectionmodule 124 (i.e., a spatial and/or temporal transform module 124), whichperforms a temporal filtering analysis and a spatial transform on thesequence of frames to eliminate transient outliers and detects at leastone object in the normalized geometric space; and, an occupancydetermination module 125, which determines the occupancy informationbased on the results or further processed results of the vehicledetection module 124; and its results can be used, for example, toderive an appropriate on-street parking pricing strategy. Embodimentsare contemplated wherein these instructions can be stored in a singlemodule or as multiple modules embodied in different devices. The modules116-125 will be later described with reference to the exemplary method.

The software modules as used herein, are intended to encompass anycollection or set of instructions executable by the occupancydetermination unit 102 or other digital system so as to configure thecomputer or other digital system to perform the task that is the intentof the software. The term “software” as used herein is intended toencompass such instructions stored in storage medium such as RAM, a harddisk, optical disk, or so forth, and is also intended to encompassso-called “firmware” that is software stored on a ROM or so forth. Suchsoftware may be organized in various ways, and may include softwarecomponents organized as libraries, internet-based programs stored on aremote server or so forth, source code, interpretive code, object code,directly executable code, and so forth. It is contemplated that thesoftware may invoke system-level code or calls to other softwareresiding on a server (not shown) or other location to perform certainfunctions. The various components of the occupancy determination unit102 may be all connected by a bus 126.

With continued reference to FIG. 2, the occupancy determination unit 102also includes one or more communication interfaces 128, such as networkinterfaces, for communicating with external devices. The communicationinterfaces 128 may include, for example, a modem, a router, a cable, andand/or Ethernet port, etc. The communication interfaces 128 are adaptedto receive the video data 130 as input.

The occupancy determination unit 102 may include one or more specialpurpose or general purpose computing devices, such as a server computer,controller, or any other computing device capable of executinginstructions for performing the exemplary method.

FIG. 2 further illustrates the occupancy determination unit 102connected to the vehicle-detection device 104 for capturing and/orproviding the video (or image frame) data in electronic format. FIG. 3is an example vehicle-detection device 104 adapted for temporarydeployment in a select parking area. The vehicle-detection device 104can be used on a mobile or stationary platform. Because thevehicle-detection device 104 is adapted to move from site-to-site, it isshown as a mobile, light-weight trailer; however, it can be integratedas a fixed pole, such as a camera attached to an existing light pole. Inthe illustrated embodiment, the trailer includes wheels 302 that can bepulled by another vehicle, but embodiments are contemplated which caninclude a framework 304 that is easily disassembled at a site locationfor transport without wheels. The vehicle-detection device 104 includesat least one image capture device 105 supported on a selectivelyadjustable mast 306. In the contemplated embodiment, at least two imagecapture devices are supported on the mast. By using multiple imagecapture devices, the vehicle-detection device 104 can establish abaseline parking occupancy pattern for a period of time. In response tothe vehicle-detection device 104 being positioned at a desired site (forexample, a street intersection), a first image capture device cancapture a front view of inbound traffic relative to the first imagecapture device and a second image capture device can capture a rear viewof outbound traffic relative to the image capture device. Detectingvehicles using multiple image capture devices can improve the efficiencyof the data collection.

More specifically, a height and angle of the image capture device 105can be fixed by adjusting the mast 306. In one embodiment, the mast 306can include telescoping piping, which can be controlled using a mastpump 308. However, embodiments are contemplated where the mast 306 canbe manually adjusted. Guide wires 310 can be used to stabilize mast 306when it reaches a desired and proper position. Anchors or similarfunctioning stabilizers 312 can firmly anchor the vehicle-detectiondevice 104 in place at the parking location.

In one embodiment, the vehicle-detection device 104 can include a powersource for powering the mast. In the illustrative example, energy isgathered by the solar panels 314 and transferred to the mast pump 308.

Returning to FIG. 2, the image capture device 105 (hereinafter “videocamera” 105) included on the mobile vehicle detection 104 may includeone or more surveillance cameras that capture video from the parkingarea-of-interest. The number of cameras may vary depending on a lengthand location of the area being monitored. It is contemplated that thecombined field of view of multiple cameras typically comprehends theentire area surrounding the parking area. For performing the method atnight, the video camera 105 can include RGB or near infrared (NIR)capabilities coupled with an external illuminator. In one contemplatedembodiment, the video camera 105 is a high resolution camera to enablethe detection of stationary objects/vehicles.

With continued reference to FIG. 2, the video data 130 undergoesprocessing by the occupancy determination unit 102 to output occupancydata (such as, statistics) 132.

Furthermore, the system 100 can display the occupancy data and/ordesired output in a suitable form on a graphic user interface (GUI) 134.The GUI 134 can include a display for displaying the information, tousers, and a user input device, such as a keyboard or touch or writablescreen, for receiving instructions as input, and/or a cursor controldevice, such as a mouse, touchpad, trackball, or the like, forcommunicating user input information and command selections to theprocessor 112. Alternatively, the occupancy determination unit 102 canprovide the occupancy data to the output device 106, which can displaypricing strategy to a user, such as a parking area management company.Furthermore, in one contemplated embodiment, the occupancy data can betransmitted to another computer application, which can performadditional processing on the image frames.

FIG. 4A-B is a flowchart describing a method 400 for determining parkingoccupancy using a selectively mobile vehicle-detection device andvision-based algorithms. The method starts at S402. During an off-linephase, multiple classifiers are trained at S404. Each classifier istrained by acquiring video of a representative parking scenario, whichis acquired for training purposes. This step can be performed offline,and the training samples can be collected off-site—from sites other thanthe actual deployment sites—or on-site—from the deployment site. Inother words, video can be captured in and acquired from any one of anoff-site surrogate parking site, on-site at a future/desired parkingsite, and a previously monitored parking site. For example, the off-sitesurrogate parking site may include an on-street parking lane. Theclassifiers are referred to as “global” classifiers because the trainingsamples can be collected from multiple sites; and the trainedclassifiers are intended to be used for stationary vehicles laterdetected and extracted from desired deployment sites that may or may nothave been used for the training at S404.

Training samples are collected by acquiring image frames of the parkingsites at S406. A training region of interest (ROI_(T)) is defined on theimage plane of the acquired training video data at S408. The ROI_(T) canbe defined manually at the time the camera is installed in the selectedtraining site. The ROI_(T) is most likely quadrilateral in shape,particularly if it includes a parking lane along a street. However, forsome embodiments, the image capture device 105 can have a viewing anglethat is wide enough to yield images having curved lines that werestraight in real-world coordinates. Therefore, the ROI_(T) can also bedefined by corner points, which can be connected by straight lines orarcs depending on the viewing angle of the image capture device.

Then, an automated algorithm is applied to the training ROI_(T) togenerate a projective correction mapping of the ROI_(T) at S410. Toderive the normalized projective transformation, the quadrilateralROT_(T) is converted into a parallelogram (such as, a “rectangularROI_(T)”) at its corner pixel coordinates, within which a width of atypical vehicle is fixed (such as, in one example, 70 pixel-wide). Forthe ROI_(T) having curved lines or defining its shape, the four cornersof the ROI_(T) can be first selected and connected by arcs. The arcs canbe defined by a third point connecting to corner points. With theROI_(T) and the current ROI being defined either by straight lines orcurves, and geometric transform (mapping) can be performed on one orboth such that they both represent the same viewing perspective.

FIG. 5A-F illustrates sample input and output of the view-normalizedtraining process. FIG. 5A shows an original front camera view of asample parking site, which is an on-street parking lane 502. FIG. 58shows a positive sample of a front-view of a stationary vehicle 504(extracted from FIG. 5A) after the view normalization. As viewablebetween the illustrations of FIGS. 5A and 58, the stationary vehicle 504adjusted from having a quadrilateral orientation relative to the cameraposition in FIG. 5A to a rectangular orientation with a fixed width.FIG. 5C shows a negative sample (no vehicle) extracted from an imageframe capturing the scene in FIG. 5A. Similarly, FIG. 5D shows anoriginal rear camera view of a sample parking site, which is anon-street parking lane 506. FIG. 5E shows a positive sample of arear-view of a stationary vehicle 508 (extracted from FIG. 5D) after theview normalization. As viewable between the illustrations of FIGS. 5Dand 5E, the stationary vehicle 508 adjusted from having a quadrilateralorientation relative to the camera position in FIG. 5D to a rectangularorientation with a fixed width. FIG. 5E shows a negative sample (novehicle) extracted from an image frame capturing the scene in FIG. 5D.The positive and negative samples can be manually labeled.

In brief, the normalized projective transformation attempts to make eachROI_(T) appear as if it was acquired from the same camera perspective.Accordingly, after the normalized projective transformation, the vehicle504 extracted from the forward-facing parking lane 502 in FIG. 5A appearas if it was acquired from the same perspective as the vehiclesextracted from any other forward-facing parking lane with differentangles shown in FIG. 5A and/or as the vehicle 508 extracted from theoppositely, oriented rear-facing parking lane 506 in FIG. 5D. Theview-normalization process aims to reduce the number of views needed totrain the classifiers.

In other embodiments, a classifier(s) can be trained using vehiclesamples normalized to one view. In the contemplated embodiment, at leasttwo classifiers can be trained each using samples from different views,such as the normalized front-view vehicle samples shown in FIG. 58 andthe normalized rear-view vehicle samples shown in FIG. 5E. The multiple(e.g., two-) view approach enables vehicles extracted from various sitesto be applied to a set of global classifiers without requiring on-sitetraining of a classifier, and without incurring significant accuracydegradation.

In more detail, a set of global parked (stationary) vehicle classifiersare trained via machine learning and the view-normalization techniques.In one contemplated embodiment, a set of HOG-SVM classifiers can betrained using a histogram of oriented gradients (HOG) as the feature andsupport vector machine (SVM) as the machine learning method. HOGfeatures have been shown to work effectively in vehicle detectionapplications; however, other combination of features (e.g., SIFT, SURF,LBP, GLOH etc.) and classifiers (e.g. LDA, Adaboost, Decision treelearning etc.) can be used for parked vehicle detection as well. Forexample, other types of classifiers that apply the features can includeNeural Nets or Decision Trees can be used.

Returning to FIG. 4A, once the vehicle-detection device 104—similar toor similar in function to that shown in FIG. 3—is positioned at a givenparking area-of-interest and the mast is adjusted to support the videocamera(s) at a desired orientation, a ROI determination module 118defines a region of interest within the parking area-of-interest as partof the camera calibration process at S412. The defined ROI decreases theregion that is later searched for stationary vehicles and therebydecreases the search time and increases the accuracy of the computerapplication. The ROI can be defined manually at the time the mast issecured in position at the deployment site. For example, the ROI can bedefined as four corners identified via the GUI 134 as user-input. TheROI is most likely defined to be quadrilateral in shape (see, forexample, FIG. 6A), which enables a view normalization process to beperformed on it. Then, an automated algorithm is applied to generate aprojective correction mapping of the ROI at S414. To derive thenormalized projective transformation, the ROI is converted into aparallelogram-shaped (e.g., rectangular shaped) ROI or block face (see,for example, FIG. 68) at its corner pixel coordinates at S416. Then,parameters for a normalized projective correction are automaticallygenerated for the block face (see, for example, FIG. 6C) at S418 withinwhich a width of a typical vehicle is fixed (such as, in one example, 70pixel-wide). The projective correction can be performed manually orautomatically off-line for the current site of deployment.

Returning to FIG. 4A, a classifier selection module 120 selects atrained classifier from among the set of pre-trained HOG-SVM classifiersat S420. As part of this selection process, the camera pose/orientationis classified relative to the current deploymentsite/parking-area-of-interest at S422. In the illustrative embodiment,the camera pose/orientation is classified as belonging to one of front-or rear-view perspective of the parking area-of-interest. For example,in the illustrative deployment site shown in FIG. 6A, the camera pose isclassified as a rear-view pose because the vehicles are generallyoriented away from the camera position. This classification can beperformed off-line and can be provided manually as user-input at thetime the system is being installed at the deployment site.Alternatively, this determination can be automated by the a classifierselection module 120, which can identify a direction of traffic flow ina lane that is next to the ROI using known video processing techniques.

The classifier selection module 120 then selects from among the set aclassifier previously trained from samples acquired from a normalizedcamera perspective similar to the normalized geometric space at S424.For example, a front-view classifier—trained using samples similar toillustrative images shown in FIGS. 5B-C—is selected if the originalimage frames capture the in response to the camera pose being classifiedas a front-view pose and the rear-view classifier—trained using samplessimilar to illustrative images shown in FIGS. 5D-E—in response to thecamera pose being classified as a rear-view pose. However, embodimentsare contemplated which do not distinguish between front and rear views,but which use only one view or use multiple other perspective views.

Returning to FIG. 4A, the video buffering module 122 acquires video databy monitoring the parking area-of-interest at S426. Generally, themodule acquires the video data from the image capture device 105, whichis being supported on the vehicle-detection device 104 that is deployedon-site. The processing of this video data is now described fordetermining the occupancy of the parking area-of-interest over time.

The vehicle detection module 124 can perform a temporal filteringanalysis on a sequence of acquired image frames to eliminate transientoutliers at S428. Because the present disclosure aims to collectstatistics and information regarding occupancy of stationary vehicleslocated in the parking area-of-interest, the method can omit processesdirected toward a detection of objects in motion. Therefore, thetemporal filtering can remove outliers, such as occlusion, that is dueto adjacent traffic, and camera shake. One approach for removingoutliers via temporal filtering is disclosed in co-pending and commonlyassigned U.S. patent application Ser. No. 13/913,606, entitled,“Precipitation Removal for Vision-Based Parking Management Systems”, byWencheng Wu, et al., filed Jun. 10, 2013, which is totally incorporatedherein by reference and which also discusses how temporal filteringimproves the performance of vehicle detection.

In the illustrative embodiment, the module 124 applies a filtering overtime at S430. A simple median filtering can be applied, althoughembodiments are contemplated as using other approaches, such astrim-mean filtering, approximate median filtering, Maximum LikelihoodEstimation (MLE), and Hidden Markov Model (HMM) estimation, etc.

Outliers are then removed from the original image frame(s) to generatefiltered image frames that essentially include stationary vehiclesegments and background at S432. FIGS. 7A-B show an image frame beforeand after the temporal filtering is performed on it. FIG. 7A shows theoriginal image frame. Transient information, such as moving commercialvehicle 702 and occlusion in the form of an open passenger vehicle door704, is visible in FIG. 7A. After the temporal filtering operation,however, these transient objects are removed. FIG. 7B shows the filteredimage frame with the outliers removed from the original frame. Theremoval of outliers enables the system to perform more robust vehicledetection in frames that originally include occlusion.

Continuing at FIG. 48, the module 124 can perform a spatial transformfor projective correction and extraction of an image segment(s) on afiltered image frame corresponding to the normalized view of the ROI atS434. The projective correction mapping used on the given filtered imageframe can be created when the ROI was defined at S412—when the systemwas installed and/or set-up at the deployment site. The normalizedprojective transformation similar to the operation described as part ofthe classification operation at S414-S418. FIG. 7C shows a defined ROIsegment 706 virtually outlined in the given filtered image in FIG. 7B.FIG. 7D shows a view-normalized ROI (image segment) for the given(illustrative) image frame. The transformed image frame in FIG. 7D isshown in grayscale, however, color-version HOGs can be used if furtherdiscrimination is needed.

Returning to FIG. 48, in one embodiment the module 124 can optionallyperform a temporal correlation on one of the transformed image content(EMB. 1) and the temporal occupancy information (EMB. 2), and fuse theresults with current occupancy information to yield a final currentoccupancy of the ROI. The temporal correlation can improve an efficiencyand accuracy of the vision-based parked vehicle detection.

In the first embodiment (EMB. 1), before searching the transformed imagesegment for at least one stationary object (i.e., vehicle), the module124 can compute a correlation R between the transformed imagesegments/normalized ROIs of two temporally consecutive frames—i.e., acurrent image frame and that of a previous frame—at S436. Thecorrelations can be compared to a predetermined threshold η₁ at S438. Inone embodiment, the threshold can approximate η₁=0.995. In response tothe correlation not exceeding the predetermined threshold (NO at S438),the vehicle detection module 124 performs vision-based stationaryvehicle detection in the normalized geometric space for the at least oneobject in the current frame at S440. Example approaches for vision-basedstationary vehicle detection are provided in the disclosures ofco-pending and commonly assigned U.S. application Ser. No. 13/922,091,entitled, “A Method For Available Parking Distance Estimation ViaVehicle Side Detection”, by Orhan Bulan, et al., filed Jun. 19, 2013 andU.S. application Ser. No. 13/835,386, entitled “Two-Dimensional AndThree-Dimensional Sliding Window-Based Methods And Systems For DetectingVehicles”, by Bulan et al., filed Mar. 15, 2013, which are each totallyincorporated herein by reference.

In response to the correlation meeting and exceeding the predeterminedthreshold (YES at S438), the module 124 can bypass the detection in thetransformed image segment/normalized ROI for the at least one object inthe current frame. Instead, the module 124 associates the occupancy ofthe current frame as being a same as the occupancy of the previous frameat S442. In other words, when the correlation is high the image contentin the current normalized ROI stripe is almost the same as the imagecontent in the previous frame. Therefore, the occupancy informationshould be the same, and there is no need for performing vision-basedparked vehicle detection.

In the second embodiment (EMB. 2), the vehicle detection module 124performs vision-based stationary vehicle detection in the normalizedgeometric space for the at least one object in the current frame atS444. The module 124 identifies a location of each pixel group where theoccupancy changes between the current and a temporally consecutiveprevious frame at S446. For each identified location, a correlationr_(i) is computed between the identified location in the current andprevious frame at S448. The correlation can be compared to apredetermined threshold η₂, at S450. In one embodiment, the thresholdcan approximate η₂=0.95. In response to the correlation not exceedingthe predetermined threshold (NO at S450), the vehicle detection module124 retains the determined occupancy of the current frame and previousframes based solely on the detected object at S442. In response to thecorrelation meeting and exceeding the predetermined threshold (YES atS450), the module 124 examines if the identified location is occupied inthe current frame and previous frames based on relative scores of theclassifier for the detected object at S452. The module 124 uses theoccupancy information with higher confidence (e.g., based on SVM-score)for that image segment at S454. In other words, for locations where theoccupancy information rendered from the vision-based stationary vehicledetection process is inconsistent between temporally consecutive frames,but where the image content is almost the same between the frames, thesystem uses the output associated with higher confidence.

The occupancy determination module 125 can be used to derive anappropriate parking pricing strategy using the occupancy information atS456. One approach for deriving the on-street parking pricing strategyis disclosed in US Publication No. 2014/0046874, entitled “Real TimeDynamic Vehicle Price Management Methods, Systems And Processor-ReadableMedia”, by Faming Li, et al., filed Aug. 8, 2012, and which is totallyincorporated herein by reference. The method ends at S458.

By applying a set of global classifiers with normalized views, thepresent disclosure eliminates the time and cost associated withre-training classifiers for each site. The normalized view enables thevision-based stationary vehicle detection to be performed within asingle search range for all deployment sites.

By applying a temporal filtering and correlation process to the videodata, the present disclosure is more robust against occlusion, rain orsnow, and camera shake, etc. The system requires no site-specificparameter tuning or re-training since the parameter values can bedirectly derived from and adjusted to other system requirements.

These aspects enable a more scalable system. The vehicle-detectiondevice 104 can revisit the parking area-of-interest once the baselineparking pattern changes. The vehicle-detection device is more costeffective since because fewer units are needed—as one unit can be usedat multiple locations—compared to the existing systems that install afixed camera at each site location.

Although the control method 400 is illustrated and described above inthe form of a series of acts or events, it will be appreciated that thevarious methods or processes of the present disclosure are not limitedby the illustrated ordering of such acts or events. In this regard,except as specifically provided hereinafter, some acts or events mayoccur in different order and/or concurrently with other acts or eventsapart from those illustrated and described herein in accordance with thedisclosure. For example, the order of which the temporal filtering andspatial transformation operations are performed on a given image framecan be interchangeable. In another example, the temporal correlation canbe performed before or after vision-based stationary vehicle detection,depending on the particular algorithms. It is further noted that not allillustrated steps may be required to implement a process or method inaccordance with the present disclosure, and one or more such acts may becombined. The illustrated methods and other methods of the disclosuremay be implemented in hardware, software, or combinations thereof, inorder to provide the control functionality described herein, and may beemployed in any system including but not limited to the aboveillustrated system 100, wherein the disclosure is not limited to thespecific applications and embodiments illustrated and described herein.

It will be appreciated that variants of the above-disclosed and otherfeatures and functions, or alternatives thereof, may be combined intomany other different systems or applications. Various presentlyunforeseen or unanticipated alternatives, modifications, variations orimprovements therein may be subsequently made by those skilled in theart which are also intended to be encompassed by the following claims.

What is claimed is:
 1. A method for estimating parking occupancy in a current parking area, the method including: deploying an image capture device in the current parking area; defining a current region of interest (ROI) within a camera field of view of the current parking area; acquiring a sequence of frames captured by the image capture device; performing a spatial transform on the current ROI to transform the current ROI in the camera field of view to a normalized geometric space; detecting at least one object in the normalized geometric space; selecting a vehicle classifier previously trained with samples acquired from a normalized camera perspective similar to the normalized geometric space; and, determining an occupancy of the current parking area by applying extracted features of the detected object to the classifier.
 2. The method of claim 1, further comprising performing a temporal filtering analysis on the sequence of frames to eliminate transient outliers.
 3. The method of claim 1, wherein the performing the spatial transform on the current ROI includes: transforming a four sided polygon to a parallelogram.
 4. The method of claim 1, further comprising: before the detecting the at least one object, computing a correlation between images in the ROI of a current frame and that of a previous frame; comparing the first correlation to a predetermined threshold; in response to the correlation not exceeding the predetermined threshold, continuing to perform the detecting in the normalized geometric space for the at least one object in the current frame; and, in response to the correlation exceeding the predetermined threshold, bypassing the detecting in the normalized geometric space for the at least one object in the current frame and associating the occupancy of the current frame as being a same as the occupancy of the previous frame.
 5. The method of claim 1, further comprising: identifying a location of each pixel group where the occupancy changes between the current and previous frames; for each identified location, computing a correlation between images of the identified location in the current and previous frames; in response to the correlation not exceeding a predetermined threshold, retaining the determined occupancy of the current frame and previous frames based solely on the detected object; in response to the correlation exceeding a predetermined threshold, examining if the identified location is occupied in the current frame and previous frames based on relative scores of the classifier for the detected object in the current frame and previous frames.
 6. The method of claim 1, further comprising: applying one of average, running average, weighted average, and median filtering to occupancy information in the current and previous frames; and, using the filtered result as the occupancy in the current frame.
 7. The method of claim 1, wherein the performing the temporal filtering analysis includes: applying a filtering over time; and, removing the outliers from the sequence of frames to generate image frames of the background objects and stationary vehicles.
 8. The method of claim 1 further comprising: applying the occupancy to a computer application to generate a demand model for managing the current parking area.
 9. The method of claim 1 further comprising training a classifier, the training including: deploying the image capture device for monitoring a representative parking area; defining a representative ROI within a camera field of view of the representative parking area; and, performing a geometric calibration on the representative ROI to transform the representative ROI in the camera field of view to a normalized geometric space within which a vehicle width is fixed at a predetermined number of pixels.
 10. The method of claim 9, wherein the training the classifier further includes: acquiring a sequence of frames captured by the image capture device; detecting stationary vehicles located in the normalized geometric space as vehicle samples; and, training at least one classifier using the vehicle samples.
 11. The method of claim 1, wherein the normalized geometric space is selected from a group consisting: a normalized front view of the current parking area; a normalized rear view of the current parking area; and, a combination including both the normalized front and rear views; and wherein the vehicle classifier is selected from a group consisting: a front view classifier; a rear view classifier; and at least two classifiers including front and rear view classifiers.
 12. A system for estimating parking occupancy, the system comprising a computer device including a memory in communication with a processor configured to: deploy an image capture device in the current parking area; define a current region of interest (ROI) within a camera field of view of the current parking area; acquire a sequence of frames captured by the image capture device; perform a spatial transform on the current ROI to transform the current ROI in the camera field of view to a normalized geometric space; detect at least one object in the normalized geometric space; select a vehicle classifier previously trained with samples acquired from a normalized camera perspective similar to the normalized geometric space; and, determine an occupancy of the current parking area by applying extracted features of the detected object to the classifier.
 13. The system of claim 12, wherein the processor is configured to perform a temporal filtering analysis on the sequence of frames to eliminate transient outliers;
 14. The system of claim 12, wherein the processor is configured to perform the spatial transform on the current ROI by transforming a four sided polygon to a parallelogram.
 15. The system of claim 12, wherein the processor is further configured to: before the detecting the at least one object, compute a correlation between images in the ROI of a current frame and that of a previous frame; compare the first correlation to a predetermined threshold; in response to the correlation not exceeding the predetermined threshold, continue to perform the detecting in the normalized geometric space for the at least one object in the current frame; and, in response to the correlation exceeding the predetermined threshold, bypass the detecting in the normalized geometric space for the at least one object in the current frame and associating the occupancy of the current frame as being a same as the occupancy of the previous frame.
 16. The system of claim 12, wherein the processor is configured to: identify a location of each pixel group where the occupancy changes between the current and previous frames; for each identified location, compute a correlation between images of the identified location in the current and previous frames; in response to the correlation not exceeding a predetermined threshold, retain the determined occupancy of the current frame and previous frames based solely on the detected object; in response to the correlation exceeding a predetermined threshold, examine if the identified location is occupied in the current frame and previous frames based on relative scores of the classifier for the detected object in the current frame and previous frames.
 17. The system of claim 12, wherein the processor is configured to: apply one of average, running average, weighted average, and median filtering to occupancy information in the current and previous frames; and, use the filtered result as the occupancy in the current frame.
 18. The system of claim 12, wherein the processor is configured to: apply a filtering over time; and, remove the outliers from the sequence of frames to generate image frames of the background objects and stationary vehicles.
 19. The system of claim 12, wherein the processor is configured to: apply the occupancy to a computer application to generate a demand model for managing the current parking area.
 20. The system of claim 12, wherein the processor is configured to: before acquiring the sequence of frames from the current parking area, deploy the image capture device for monitoring a representative parking area, wherein the representative parking area can be a same as or different from the current parking area; define a representative ROI within a camera field of view of the representative parking area; and, perform a geometric calibration on the representative ROI to transform the representative ROI in the camera field of view to a normalized geometric space within which a vehicle width is fixed at a predetermined number of pixels.
 21. The system of claim 12, wherein the processor is configured to: acquire a sequence of frames captured by the image capture device; detect stationary vehicles located in the normalized geometric space as vehicle samples; and, train at least one classifier using the vehicle samples.
 22. The system of claim 12, wherein the normalized geometric space is selected from a group consisting: a normalized front view of the current parking area; a normalized rear view of the current parking area; and, a combination including both the normalized front and rear views; and wherein the vehicle classifier is selected from a group consisting: a front view classifier; a rear view classifier; and at least two classifiers including front and rear view classifiers.
 23. A system for estimating parking occupancy, the system comprising: a selectively mobile vehicle-detection device, the vehicle-detection device including: at least one image capture device for acquiring video of a current parking area, and an adjustable mast supporting the at least one image capture device at a select height; and a computer processor in communication with the image capture device, the computer processor configured to: receive the video data; define a region of interest in the video data; perform a spatial transform on the ROI to transform the ROI to a normalized geometric space; apply features of a detected object in the normalized geometric space to a vehicle classifier previously trained with samples acquired from a normalized camera perspective similar to the normalized geometric space; and, determine the occupancy of the current parking area using an output of the classifier. 